The use of feature scaling in scikit learn
Short answer: if you apply the same scaling method as what the algorithm does, it won't change anything. If you choose a different scaling method, then you'll end up performing two transforms on the original data, which may not be a bad thing depending on the context. 

This implies that you have to know what kind of scaling the algorithm performs.

As a takeaway, it is good practice to scale the data beforehand yourself, so that you are sure what you input the algorithm with. An scaling rarely arms your model results.

As a note (I know this is not your question, but it may help), you have to understand why you scale the data. Taking the simple case of a linear regression, if your input features are house size and number of rooms and say you want to build a model of the house price from those features, number of rooms and size will have very different ranges. Scaling will help to:


make the algorithm converge more rapidly
allow you to answer questions such as "what is the main contributor to the price" by comparing the coefficients (this would be incorrect w/o scaling beforehand)