diff --git a/docs/usage_guide/optimization_notes/index.rst b/docs/usage_guide/optimization_notes/index.rst
index 0dcbe5a25d7..dd9ed22352e 100644
--- a/docs/usage_guide/optimization_notes/index.rst
+++ b/docs/usage_guide/optimization_notes/index.rst
@@ -314,6 +314,36 @@ Copy-pastable example, showing how mixing pandas and Modin DataFrames in a singl
   # Possible output: TypeError
 
 
+Execute dataframe operations using pandas NativeQueryCompiler
+""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
+By default, Modin distributes every dataframe across partitions and performs operations
+using the PandasQueryCompiler. However, for certain scenarios such as handling small or empty dataframes,
+distributing them may introduce unnecessary overhead. In such cases, it's more efficient to default
+to Pandas at the query compiler level. This can be achieved by setting the cfg.NativeDataframeMode
+:doc:`configuration variable: </flow/modin/config> to "Pandas". When enabled, all operations in Modin default to Pandas, and the dataframes are not distributed,
+avoiding additional overhead. This configuration can be toggled on or off depending on whether
+dataframe distribution is required.
+
+Dataframes created while the NativeDataframeMode is active will continue to use the NativeQueryCompiler
+even after the config is disabled. Modin supports interoperability between distributed Modin dataframes and
+ those using the NativeQueryCompiler.
+
+.. code-block:: python
+
+  import modin.pandas as pd
+  import modin.config as cfg
+
+  # This dataframe will be distributed and use `PandasQueryCompiler` by default
+  df_distributed = pd.DataFrame(...)
+
+  # Set mode to "Pandas" to avoid distribution and use `NativeQueryCompiler`
+  cfg.NativeDataframeMode.put("Pandas")
+  df_native_qc = pd.DataFrame(...)
+
+  # Revert to default settings for distributed dataframes
+  cfg.NativeDataframeMode.put("Default")
+  df_distributed = pd.DataFrame(...)
+
 Operation-specific optimizations
 """"""""""""""""""""""""""""""""