diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst
index 83ba21e013e2e..f25973f14181f 100644
--- a/doc/source/user_guide/indexing.rst
+++ b/doc/source/user_guide/indexing.rst
@@ -20,15 +20,17 @@ this area.
 
 .. note::
 
-   The Python and NumPy indexing operators ``[]`` and attribute operator ``.``
-   provide quick and easy access to pandas data structures across a wide range
-   of use cases. This makes interactive work intuitive, as there's little new
-   to learn if you already know how to deal with Python dictionaries and NumPy
+   The Python and NumPy indexing operators ``[]`` and the attribute operator ``.``
+   provide quick and easy access to pandas data structures across a wide range of
+   use cases. This makes interactive work intuitive, as there's little new to
+   learn if you already know how to deal with Python dictionaries and NumPy
    arrays. However, since the type of the data to be accessed isn't known in
-   advance, directly using standard operators has some optimization limits. For
-   production code, we recommended that you take advantage of the optimized
-   pandas data access methods exposed in this chapter.
+   advance, directly using these standard operators has some optimization limits.
 
+   For performance-critical or production code, we recommend using the optimized
+   pandas data access methods (such as ``.loc`` and ``.iloc``) described in this
+   chapter.
+   
 See the :ref:`MultiIndex / Advanced Indexing <advanced>` for ``MultiIndex`` and more advanced indexing documentation.
 
 See the :ref:`cookbook<cookbook.selection>` for some advanced strategies.
diff --git a/doc/source/user_guide/text.rst b/doc/source/user_guide/text.rst
index 8f404dbf461c8..ebe8201955089 100644
--- a/doc/source/user_guide/text.rst
+++ b/doc/source/user_guide/text.rst
@@ -91,6 +91,41 @@ See :ref:`text.four_string_variants` section below for details.
 
 .. _text.string_methods:
 
+String storage: pyarrow vs python
+---------------------------------
+
+Pandas supports different storage backends for string data.
+Depending on the configuration and installed dependencies,
+string data may be stored using either a Python object-based
+implementation or a pyarrow-backed implementation.
+
+In general, the pyarrow-backed string storage is recommended
+for most users, as it provides better performance and a more
+compact memory representation.
+
+**pyarrow-backed string storage**
+
+- Pros:
+  - More compact memory footprint
+  - Faster vectorized string operations
+- Cons:
+  - Strings are immutable; modifying values results in new arrays
+  - Some edge-case behavior differences compared to Python strings
+
+**Python object string storage**
+
+- Pros:
+  - Uses Python string objects (mutable at the array level)
+  - Behavior consistent with standard Python string semantics
+- Cons:
+  - Higher memory usage
+  - Slower performance due to lack of vectorization
+
+While pandas aims to provide identical results regardless of
+the underlying string storage, some behavior differences may
+exist in edge cases (for example, certain Unicode operations).
+These differences are documented where relevant.
+
 String methods
 ==============