How to normalize just one feature by scikit-learn?
As mentioned, the easiest way is to apply the StandardScaler to only the subset of features that need to be scaled, and then concatenate the result with the remaining features.

Alternatively, scikit-learn also offers (a still experimental, i.e. subject to change) ColumnTransformer API. It works similar to a pipline:

from sklearn.compose import ColumnTransformer 
from sklearn.preprocessing import  StandarScaler 
column_trans = ColumnTransformer(
    [('scaler', StandardScaler(),2],
    remainder='passthrough') 
column_trans.fit_transform(X)


The third entry of the tuple (here : 2) can be a single or a list of column indices (or column names when working with a pandas DataFrame).