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diff --git a/doc/conf.py b/doc/conf.py
@@ -281,6 +281,11 @@ def __repr__(self):
 
     def __call__(self, directory):
         src_path = os.path.normpath(os.path.join(self.src_dir, directory))
+
+        # Forces Release Highlights to the top
+        if os.path.basename(src_path) == "release_highlights":
+            return "0"
+
         readme = os.path.join(src_path, "README.txt")
 
         try:

diff --git a/doc/developers/plotting.rst b/doc/developers/plotting.rst
@@ -50,7 +50,7 @@ attributes::
                                 estimator.__class__.__name__)
        return viz.plot(ax=ax, name=name, **kwargs)
 
-Read more in :ref:`sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`
+Read more in :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
 and the :ref:`User Guide <visualizations>`.
 
 Plotting with Multiple Axes

diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
@@ -144,7 +144,7 @@ or by name::
  * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
  * :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`
  * :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py`
- * :ref:`sphx_glr_auto_examples_plot_kernel_approximation.py`
+ * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
  * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
  * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`
 

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
@@ -246,7 +246,7 @@ amount of time (e.g., on large datasets).
 
  * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py`
  * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`
- * :ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`
+ * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
 
 .. topic:: References
 

diff --git a/doc/modules/isotonic.rst b/doc/modules/isotonic.rst
@@ -28,6 +28,6 @@ correlation coefficient
 for predicting to unseen data. The predictions of :class:`IsotonicRegression`
 thus form a function that is piecewise linear:
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_isotonic_regression_001.png
-   :target: ../auto_examples/plot_isotonic_regression.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_isotonic_regression_001.png
+   :target: ../auto_examples/miscellaneous/plot_isotonic_regression.html
    :align: center
diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst
@@ -84,16 +84,16 @@ For a given value of ``n_components`` :class:`RBFSampler` is often less accurate
 as :class:`Nystroem`. :class:`RBFSampler` is cheaper to compute, though, making
 use of larger feature spaces more efficient.
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_kernel_approximation_002.png
-    :target: ../auto_examples/plot_kernel_approximation.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_kernel_approximation_002.png
+    :target: ../auto_examples/miscellaneous/plot_kernel_approximation.html
     :scale: 50%
     :align: center
 
     Comparing an exact RBF kernel (left) with the approximation (right)
 
 .. topic:: Examples:
 
-    * :ref:`sphx_glr_auto_examples_plot_kernel_approximation.py`
+    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
 
 .. _additive_chi_kernel_approx:
 

diff --git a/doc/modules/kernel_ridge.rst b/doc/modules/kernel_ridge.rst
@@ -35,8 +35,8 @@ However, prediction of 100000 target values is more than three times faster
 with :class:`~sklearn.svm.SVR` since it has learned a sparse model using only
 approximately 1/3 of the 100 training datapoints as support vectors.
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_kernel_ridge_regression_001.png
-   :target: ../auto_examples/plot_kernel_ridge_regression.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_kernel_ridge_regression_001.png
+   :target: ../auto_examples/miscellaneous/plot_kernel_ridge_regression.html
    :align: center
 
 The next figure compares the time for fitting and prediction of
@@ -51,8 +51,8 @@ prediction time depends on the parameters :math:`\epsilon` and :math:`C` of
 the :class:`~sklearn.svm.SVR`; :math:`\epsilon = 0` would correspond to a
 dense model.
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_kernel_ridge_regression_002.png
-   :target: ../auto_examples/plot_kernel_ridge_regression.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_kernel_ridge_regression_002.png
+   :target: ../auto_examples/miscellaneous/plot_kernel_ridge_regression.html
    :align: center
 
 

diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst
@@ -311,15 +311,15 @@ To use this feature, feed the classifier an indicator matrix, in which cell
 [i, j] indicates the presence of label j in sample i.
 
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_multilabel_001.png
-    :target: ../auto_examples/plot_multilabel.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multilabel_001.png
+    :target: ../auto_examples/miscellaneous/plot_multilabel.html
     :align: center
     :scale: 75%
 
 
 .. topic:: Examples:
 
-    * :ref:`sphx_glr_auto_examples_plot_multilabel.py`
+    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`
 
 .. _ovo_classification:
 

diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
@@ -230,12 +230,12 @@ which will be used to compute the weights.
    :scale: 75
 
 The use of multi-output nearest neighbors for regression is demonstrated in
-:ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`. In this example, the inputs
+:ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`. In this example, the inputs
 X are the pixels of the upper half of faces and the outputs Y are the pixels of
 the lower half of those faces.
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_multioutput_face_completion_001.png
-   :target: ../auto_examples/plot_multioutput_face_completion.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png
+   :target: ../auto_examples/miscellaneous/plot_multioutput_face_completion.html
    :scale: 75
    :align: center
 
@@ -245,7 +245,7 @@ the lower half of those faces.
   * :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`: an example of regression
     using nearest neighbors.
 
-  * :ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`: an example of
+  * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`: an example of
     multi-output regression using nearest neighbors.
 
 

diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst
@@ -98,8 +98,8 @@ Outlier Factor (LOF) does not show a decision boundary in black as it
 has no predict method to be applied on new data when it is used for outlier
 detection.
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_anomaly_comparison_001.png
-   :target: ../auto_examples/plot_anomaly_comparison.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_anomaly_comparison_001.png
+   :target: ../auto_examples/miscellaneous/plot_anomaly_comparison.html
    :align: center
    :scale: 50
 
@@ -109,12 +109,12 @@ The :class:`svm.OneClassSVM` is known to be sensitive to outliers and thus
 does not perform very well for outlier detection. Finally,
 :class:`covariance.EllipticEnvelope` assumes the data is Gaussian and learns
 an ellipse. For more details on the different estimators refer to the example
-:ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` and the sections
-hereunder.
+:ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` and the
+sections hereunder.
 
 .. topic:: Examples:
 
-  * See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py`
+  * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
     for a comparison of the :class:`svm.OneClassSVM`, the
     :class:`ensemble.IsolationForest`, the
     :class:`neighbors.LocalOutlierFactor` and
@@ -270,8 +270,8 @@ allows you to add more trees to an already fitted model::
    * See :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` for
      an illustration of the use of IsolationForest.
 
-   * See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` for a
-     comparison of :class:`ensemble.IsolationForest` with
+   * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
+     for a comparison of :class:`ensemble.IsolationForest` with
      :class:`neighbors.LocalOutlierFactor`,
      :class:`svm.OneClassSVM` (tuned to perform like an outlier detection
      method) and a covariance-based outlier detection with
@@ -339,8 +339,8 @@ This strategy is illustrated below.
    * See :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py`
      for an illustration of the use of :class:`neighbors.LocalOutlierFactor`.
 
-   * See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` for a
-     comparison with other anomaly detection methods.
+   * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
+     for a comparison with other anomaly detection methods.
 
 .. topic:: References:
 

diff --git a/doc/modules/random_projection.rst b/doc/modules/random_projection.rst
@@ -64,19 +64,19 @@ bounded distortion introduced by the random projection::
   >>> johnson_lindenstrauss_min_dim(n_samples=[1e4, 1e5, 1e6], eps=0.1)
   array([ 7894,  9868, 11841])
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_johnson_lindenstrauss_bound_001.png
-   :target: ../auto_examples/plot_johnson_lindenstrauss_bound.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_johnson_lindenstrauss_bound_001.png
+   :target: ../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
    :scale: 75
    :align: center
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_johnson_lindenstrauss_bound_002.png
-   :target: ../auto_examples/plot_johnson_lindenstrauss_bound.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_johnson_lindenstrauss_bound_002.png
+   :target: ../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
    :scale: 75
    :align: center
 
 .. topic:: Example:
 
-  * See :ref:`sphx_glr_auto_examples_plot_johnson_lindenstrauss_bound.py`
+  * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
     for a theoretical explication on the Johnson-Lindenstrauss lemma and an
     empirical validation using sparse random matrices.
 

diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst
@@ -280,19 +280,19 @@ X is a single real value and the outputs Y are the sine and cosine of X.
    :align: center
 
 The use of multi-output trees for classification is demonstrated in
-:ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`. In this example, the inputs
+:ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`. In this example, the inputs
 X are the pixels of the upper half of faces and the outputs Y are the pixels of
 the lower half of those faces.
 
-.. figure:: ../auto_examples/images/sphx_glr_plot_multioutput_face_completion_001.png
-   :target: ../auto_examples/plot_multioutput_face_completion.html
+.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png
+   :target: ../auto_examples/miscellaneous/plot_multioutput_face_completion.html
    :scale: 75
    :align: center
 
 .. topic:: Examples:
 
  * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`
- * :ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`
+ * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
 
 .. topic:: References:
 

diff --git a/doc/modules/unsupervised_reduction.rst b/doc/modules/unsupervised_reduction.rst
@@ -37,7 +37,7 @@ documentation: :ref:`random_projection`.
 
 .. topic:: **Examples**
 
-   * :ref:`sphx_glr_auto_examples_plot_johnson_lindenstrauss_bound.py`
+   * :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
 
 Feature agglomeration
 ------------------------

diff --git a/doc/visualizations.rst b/doc/visualizations.rst
@@ -24,8 +24,8 @@ ROC curve for a fitted support vector machine:
 
     svc_disp = plot_roc_curve(svc, X_test, y_test)
 
-.. figure:: auto_examples/images/sphx_glr_plot_roc_curve_visualization_api_001.png
-    :target: auto_examples/plot_roc_curve_visualization_api.html
+.. figure:: auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png
+    :target: auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
     :align: center
     :scale: 75%
 
@@ -48,8 +48,8 @@ method of the `Display` object.
     rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=ax, alpha=0.8)
     svc_disp.plot(ax=ax, alpha=0.8)
 
-.. figure:: auto_examples/images/sphx_glr_plot_roc_curve_visualization_api_002.png
-    :target: auto_examples/plot_roc_curve_visualization_api.html
+.. figure:: auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png
+    :target: auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
     :align: center
     :scale: 75%
 
@@ -58,9 +58,9 @@ values of the curves.
 
 .. topic:: Examples:
 
-    * :ref:`sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`
-    * :ref:`sphx_glr_auto_examples_plot_partial_dependence_visualization_api.py`
-    * :ref:`sphx_glr_auto_examples_plot_display_object_visualization.py`
+    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
+    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
+    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`
 
 Available Plotting Utilities
 ============================

diff --git a/examples/README.txt b/examples/README.txt
@@ -2,8 +2,3 @@
 
 Examples
 ========
-
-Miscellaneous examples
-----------------------
-
-Miscellaneous and introductory examples for scikit-learn.
diff --git a/examples/miscellaneous/README.txt b/examples/miscellaneous/README.txt
@@ -0,0 +1,7 @@
+.. _miscellaneous_examples:
+
+Miscellaneous
+-------------
+
+Miscellaneous and introductory examples for scikit-learn.
+
diff --git a/examples/plot_anomaly_comparison.py → .../miscellaneous/plot_anomaly_comparison.py b/examples/plot_anomaly_comparison.py → .../miscellaneous/plot_anomaly_comparison.py
diff --git a/...les/plot_changed_only_pprint_parameter.py → ...ous/plot_changed_only_pprint_parameter.py b/...les/plot_changed_only_pprint_parameter.py → ...ous/plot_changed_only_pprint_parameter.py
diff --git a/...ples/plot_display_object_visualization.py → ...eous/plot_display_object_visualization.py b/...ples/plot_display_object_visualization.py → ...eous/plot_display_object_visualization.py
diff --git a/examples/plot_isotonic_regression.py → ...miscellaneous/plot_isotonic_regression.py b/examples/plot_isotonic_regression.py → ...miscellaneous/plot_isotonic_regression.py
diff --git a/examples/plot_johnson_lindenstrauss_bound.py → ...neous/plot_johnson_lindenstrauss_bound.py b/examples/plot_johnson_lindenstrauss_bound.py → ...neous/plot_johnson_lindenstrauss_bound.py
@@ -8,7 +8,8 @@
 dataset can be randomly projected into a lower dimensional Euclidean
 space while controlling the distortion in the pairwise distances.
 
-.. _`Johnson-Lindenstrauss lemma`: https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
+.. _`Johnson-Lindenstrauss lemma`: https://en.wikipedia.org/wiki/\
+    Johnson%E2%80%93Lindenstrauss_lemma
 """
 
 print(__doc__)

diff --git a/examples/plot_kernel_approximation.py → ...iscellaneous/plot_kernel_approximation.py b/examples/plot_kernel_approximation.py → ...iscellaneous/plot_kernel_approximation.py
diff --git a/examples/plot_kernel_ridge_regression.py → ...ellaneous/plot_kernel_ridge_regression.py b/examples/plot_kernel_ridge_regression.py → ...ellaneous/plot_kernel_ridge_regression.py
diff --git a/examples/plot_multilabel.py → examples/miscellaneous/plot_multilabel.py b/examples/plot_multilabel.py → examples/miscellaneous/plot_multilabel.py
diff --git a/examples/plot_multioutput_face_completion.py → ...neous/plot_multioutput_face_completion.py b/examples/plot_multioutput_face_completion.py → ...neous/plot_multioutput_face_completion.py
diff --git a/...t_partial_dependence_visualization_api.py → ...t_partial_dependence_visualization_api.py b/...t_partial_dependence_visualization_api.py → ...t_partial_dependence_visualization_api.py
@@ -10,9 +10,9 @@
 
 .. note::
 
-    See also :ref:`sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`
+    See also :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
 
-"""
+"""  # noqa
 print(__doc__)
 
 import pandas as pd

diff --git a/examples/plot_roc_curve_visualization_api.py → ...neous/plot_roc_curve_visualization_api.py b/examples/plot_roc_curve_visualization_api.py → ...neous/plot_roc_curve_visualization_api.py
diff --git a/sklearn/inspection/_plot/partial_dependence.py b/sklearn/inspection/_plot/partial_dependence.py
@@ -343,7 +343,7 @@ class PartialDependenceDisplay:
     stored as attributes.
 
     Read more in
-    :ref:`sphx_glr_auto_examples_plot_partial_dependence_visualization_api.py`
+    :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
     and the :ref:`User Guide <visualizations>`.
 
         .. versionadded:: 0.22