Features standardization - Multilayer perceptron
Generally, preprocessing parameters are fit only on the training subset, because otherwise you could overfit your data and overestimate quality of your model on the test subset.

With feature standardization, however, overfitting is not so dangerous, so I assume you can preprocess all your dataset at once safely.

The best practice, however, is to do all the processing in one pipeline and apply cross-validation to the whole pipeline:

from sklearn.pipeline import make_pipeline
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score, GridSearchCV
scaler = StandardScaler()  
model = MLPClassifier()
pipeline = make_pipeline(scaler, model)
scores = cross_val_score(pipeline, X, y)


If you use scikit-learn cross-validation, incorporating pipelines would be simple.