sklearn learning_curve and StandardScaler
learning_curve does not implement StandardScaler on its own.  You could create a Pipeline as your estimator where the first step is StandardScaler then whatever your estimator you're using as the next step.  This way when you call learning_curve during each cv iteration you are training both the scaler and estimator on the training folds and performance is being validated against the testing fold in each iteration.
You would not want to scale the entire dataset before calling learning_curve.  The reason is when you scale the entire set before training your model you are introducing bias because you are using data that will be used for validation to train the model, which can cause over-fitting.
I want to know if the sklearn.model_selection learning_curve can use or does use sklearn.preprocessing StandardScaler.  I've looked over the implementation, but my skill level isn't up to par to come to a conclusion on my own.  All tutorials on using learning_curve have you pass the entire data set to the learning_curve and learning_curve will split the data into training and testing sets.
All tutorials for any estimators have you split the data into training and test then scale only the training data and transform the test data using the training data scale.  Which completely understand.
Should I scale the entire data set before passing it to learn_curve.  I do know learning_curve will use either k-folds or some other cross validation method, so does it even matter because it will all get averaged out with cross validation?
Thanks,