diff --git a/doc/source/user_guide/computation.rst b/doc/source/user_guide/computation.rst
index d7875e5b8d861..151ef36be7c98 100644
--- a/doc/source/user_guide/computation.rst
+++ b/doc/source/user_guide/computation.rst
@@ -361,6 +361,9 @@ compute the mean absolute deviation on a rolling basis:
    @savefig rolling_apply_ex.png
    s.rolling(window=60).apply(mad, raw=True).plot(style='k')
 
+Using the Numba engine
+~~~~~~~~~~~~~~~~~~~~~~
+
 .. versionadded:: 1.0
 
 Additionally, :meth:`~Rolling.apply` can leverage `Numba <https://numba.pydata.org/>`__
diff --git a/doc/source/user_guide/enhancingperf.rst b/doc/source/user_guide/enhancingperf.rst
index 24fcb369804c6..9e101c1a20371 100644
--- a/doc/source/user_guide/enhancingperf.rst
+++ b/doc/source/user_guide/enhancingperf.rst
@@ -373,6 +373,13 @@ nicer interface by passing/returning pandas objects.
 
 In this example, using Numba was faster than Cython.
 
+Numba as an argument
+~~~~~~~~~~~~~~~~~~~~
+
+Additionally, we can leverage the power of `Numba <https://numba.pydata.org/>`__
+by calling it as an argument in :meth:`~Rolling.apply`. See :ref:`Computation tools
+<stats.rolling_apply>` for an extensive example.
+
 Vectorize
 ~~~~~~~~~