DOC: fix SA01,ES01 for pandas.NaT (#59660)

ci/code_checks.sh

@@ -71,7 +71,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i ES01 `# For now it is ok if docstrings are missing the extended summary` \
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
         -i "pandas.NA SA01" \
-        -i "pandas.NaT SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
         -i "pandas.Period.strftime PR01,SA01" \

pandas/_libs/tslibs/nattype.pyx

@@ -348,6 +348,22 @@ class NaTType(_NaT):
     """
     (N)ot-(A)-(T)ime, the time equivalent of NaN.
 
+    NaT is used to denote missing or null values in datetime and timedelta objects
+    in pandas. It functions similarly to how NaN is used for numerical data.
+    Operations with NaT will generally propagate the NaT value, similar to NaN.
+    NaT can be used in pandas data structures like Series and DataFrame
+    to represent missing datetime values. It is useful in data analysis
+    and time series analysis when working with incomplete or sparse
+    time-based data. Pandas provides robust handling of NaT to ensure
+    consistency and reliability in computations involving datetime objects.
+
+    See Also
+    --------
+    NA : NA ("not available") missing value indicator.
+    isna : Detect missing values (NaN or NaT) in an array-like object.
+    notna : Detect non-missing values.
+    numpy.nan : Floating point representation of Not a Number (NaN) for numerical data.
+
     Examples
     --------
     >>> pd.DataFrame([pd.Timestamp("2023"), np.nan], columns=["col_1"])