classificator using sample of a population: scaling the population and then sampling / scaling the sample / scaling the X_TRAIN split of the sample?
Wonderful question. I had similar questions in my mind when I had started out few years ago. Let me try and give my two cents on this.
I suggest to go with creating a scaler for scaling X_train, store the scaler and see if use it to transform X_test. According to centrality theorem, if you have done random sampling, you should have a mean and variance similar to the population attributes. The scaler works based on these two parameters in most cases. If it is representative of the population parameters, as long as the test data is coming from same population the scaler should work. If it is not working, you need more samples for training or another sampling attempt to get X_train representative of population. 
By doing this, you are sure the model is going to work on new samples also as long as it is generated by same process. After all, the model is not built to be tested it is meant to be in production doing some useful work.
My recommendation would be to go with 3) scaling X_train and use the scaler to transform X_test.
I am building a logistic regression classificator.
I start form a set of 500.000 record and I want to use only a sample of them.
what do you recommend:
1) scaling the population and then sampling 
2) scaling the sample 
3) scaling just the X_TRAIN split of the sample?
and why?
my considerations are:
1) this may have sense if the sample is representative of the population (should I test it?) 
2) this is not convincing because I would go for several sample in order to see the generalization level of the classificator, and having everytime a slightly different scaler does not sounds good. plus it will bias the X_train, X_test split
3) This will not bias the X_train, X_test split but has the same doubt of the point2)
What  would you recommend and why?