diff --git a/doc/source/index.rst b/doc/source/index.rst
index 950ad37a1d22..570f4a594947 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -312,6 +312,7 @@ Papers
    joblib.rst
    iter.rst
    xgboost-ray.rst
+   modin/index.rst
    dask-on-ray.rst
    mars-on-ray.rst
    raydp.rst
diff --git a/doc/source/modin/index.rst b/doc/source/modin/index.rst
new file mode 100644
index 000000000000..f7e62fc3f540
--- /dev/null
+++ b/doc/source/modin/index.rst
@@ -0,0 +1,97 @@
+Modin (Pandas on Ray)
+=====================
+
+Modin_, previously Pandas on Ray, is a dataframe manipulation library that
+allows users to speed up their pandas workloads by acting as a drop-in
+replacement. Modin also provides support for other APIs (e.g. spreadsheet)
+and libraries, like xgboost.
+
+.. code-block:: python
+
+   import modin.pandas as pd
+   import ray
+
+   ray.init()
+   df = pd.read_parquet("s3://my-bucket/big.parquet")
+
+You can use Modin on Ray with your laptop or cluster. In this document,
+we show instructions for how to set up a Modin compatible Ray cluster
+and connect Modin to Ray.
+
+.. note:: In previous versions of Modin, you had to initialize Ray before importing Modin. As of Modin 0.9.0, This is no longer the case.
+
+Using Modin with Ray's autoscaler
+---------------------------------
+
+In order to use Modin with :ref:`Ray's autoscaler <cluster-index>`, you need to ensure that the
+correct dependencies are installed at startup. Modin's repository has an
+example `yaml file and set of tutorial notebooks`_ to ensure that the Ray
+cluster has the correct dependencies. Once the cluster is up, connect Modin
+by simply importing.
+
+.. code-block:: python
+
+   import modin.pandas as pd
+   import ray
+
+   ray.init(address="auto")
+   df = pd.read_parquet("s3://my-bucket/big.parquet")
+
+As long as Ray is initialized before any dataframes are created, Modin
+will be able to connect to and use the Ray cluster.
+
+Modin with the Ray Client
+-------------------------
+
+When using Modin with the :ref:`Ray Client <ray-client>`, it is important to ensure that the
+cluster has all dependencies installed.
+
+.. code-block:: python
+
+   import modin.pandas as pd
+   import ray
+   import ray.util
+
+   ray.util.connect()
+   df = pd.read_parquet("s3://my-bucket/big.parquet")
+
+Modin will automatically use the Ray Client for computation when the file
+is read.
+
+How Modin uses Ray
+------------------
+
+Modin has a layered architecture, and the core abstraction for data manipulation
+is the Modin Dataframe, which implements a novel algebra that enables Modin to
+handle all of pandas (see Modin's documentation_ for more on the architecture).
+Modin's internal dataframe object has a scheduling layer that is able to partition
+and operate on data with Ray.
+
+Dataframe operations
+''''''''''''''''''''
+
+The Modin Dataframe uses Ray tasks to perform data manipulations. Ray Tasks have
+a number of benefits over the actor model for data manipulation:
+
+- Multiple tasks may be manipulating the same objects simultaneously
+- Objects in Ray's object store are immutable, making provenance and lineage easier
+  to track
+- As new workers come online the shuffling of data will happen as tasks are
+  scheduled on the new node
+- Identical partitions need not be replicated, especially beneficial for operations
+  that selectively mutate the data (e.g. ``fillna``).
+- Finer grained parallelism with finer grained placement control
+
+Machine Learning
+''''''''''''''''
+
+Modin uses Ray Actors for the machine learning support it currently provides.
+Modin's implementation of XGBoost is able to spin up one actor for each node
+and aggregate all of the partitions on that node to the XGBoost Actor. Modin
+is able to specify precisely the node IP for each actor on creation, giving
+fine-grained control over placement - a must for distributed training
+performance.
+
+.. _Modin: https://github.com/modin-project/modin
+.. _documentation: https://modin.readthedocs.io/en/latest/developer/architecture.html
+.. _yaml file and set of tutorial notebooks: https://github.com/modin-project/modin/tree/master/examples/tutorial/tutorial_notebooks/cluster
diff --git a/doc/source/ray-client.rst b/doc/source/ray-client.rst
index a0335faaef1d..eb094d204aa8 100644
--- a/doc/source/ray-client.rst
+++ b/doc/source/ray-client.rst
@@ -1,3 +1,5 @@
+.. _ray-client:
+
 **********
 Ray Client
 **********
@@ -33,8 +35,7 @@ From here, another Ray script can access that server from a networked machine wi
    do_work.remote(2)
    #....
 
-When the client disconnects, any object or actor references held by the server on behalf of the client are dropped, as if directly disconnecting from the cluster
-
+When the client disconnects, any object or actor references held by the server on behalf of the client are dropped, as if directly disconnecting from the cluster.
 
 ===================
 ``RAY_CLIENT_MODE``