diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 9faa2a249613b..cf9c7401b1ee3 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -114,7 +114,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.PerformanceWarning SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
-        -i "pandas.infer_freq SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
         -i "pandas.io.stata.StataWriter.write_file SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py
index 534bee5fede44..9a01568971af8 100644
--- a/pandas/tseries/frequencies.py
+++ b/pandas/tseries/frequencies.py
@@ -89,6 +89,11 @@ def infer_freq(
     """
     Infer the most likely frequency given the input index.
 
+    This method attempts to deduce the most probable frequency (e.g., 'D' for daily,
+    'H' for hourly) from a sequence of datetime-like objects. It is particularly useful
+    when the frequency of a time series is not explicitly set or known but can be
+    inferred from its values.
+
     Parameters
     ----------
     index : DatetimeIndex, TimedeltaIndex, Series or array-like
@@ -106,6 +111,13 @@ def infer_freq(
     ValueError
         If there are fewer than three values.
 
+    See Also
+    --------
+    date_range : Return a fixed frequency DatetimeIndex.
+    timedelta_range : Return a fixed frequency TimedeltaIndex with day as the default.
+    period_range : Return a fixed frequency PeriodIndex.
+    DatetimeIndex.freq : Return the frequency object if it is set, otherwise None.
+
     Examples
     --------
     >>> idx = pd.date_range(start="2020/12/01", end="2020/12/30", periods=30)