diff --git a/docs/source/pages/overview.rst b/docs/source/pages/overview.rst
index 6fa6aeb256b..649f755b366 100644
--- a/docs/source/pages/overview.rst
+++ b/docs/source/pages/overview.rst
@@ -361,6 +361,50 @@ information on this topic.
 .. autoclass:: torchmetrics.MetricCollection
     :exclude-members: update, compute, forward
 
+***************
+Metric wrappers
+***************
+
+In some cases it is beneficial to transform the output of one metric in some way or add additional logic. For this we
+have implemented a few *Wrapper* metrics. Wrapper metrics always take another :class:`~torchmetrics.Metric` or (
+:class:`~torchmetrics.MetricCollection`) as input and wraps it in some way. A good example of this is the
+:class:`~torchmetrics.wrappers.ClasswiseWrapper` that allows for easy altering the output of certain classification
+metrics to also include label information.
+
+.. testcode::
+    from torchmetrics.classification import MulticlassAccuracy
+    from torchmetrics.wrappers import ClasswiseWrapper
+    base_metric = MulticlassAccuracy(num_classes=3, average=None)
+    wrapped_metric = ClasswiseWrapper(base_metric, labels=["cat", "dog", "fish"])
+    target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
+    preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
+    print(base_metric(preds, target))  # this returns a simple tensor without label info
+    print(wrapped_metric(preds, target))  # this returns a dict with label info
+
+.. testoutput::
+    :options: +NORMALIZE_WHITESPACE
+
+    torch.tensor([0.0000, 0.0000, 0.3333]
+    {'multiclassaccuracy_cat': tensor(0.),
+     'multiclassaccuracy_dog': tensor(0.),
+     'multiclassaccuracy_fish': tensor(0.3333)}
+
+Another good example of wrappers is the :class:`~torchmetrics.wrappers.BootStrapper` that allows for easy bootstrapping
+of metrics e.g. computation of confidence intervals by resampling of input data.
+
+.. testcode::
+    from torchmetrics.classification import MulticlassAccuracy
+    from torchmetrics.wrappers import ClasswiseWrapper
+    wrapped_metric = BootStrapper(MulticlassAccuracy(num_classes=3))
+    target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
+    preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
+
+.. testoutput::
+    :options: +NORMALIZE_WHITESPACE
+
+    {'mean': tensor(0.1219), 'std': tensor(0.0217)}
+
+You can see all implemented wrappers under the wrapper section of the API docs.
 
 ****************************
 Module vs Functional Metrics