In Machine Learning, what is the point of using stratified sampling in selecting test set data?
When the distribution of your data is balanced or you have enough samples of each class, a normal shuffle split will work well. But if your data distribution is unbalanced and one of the classes is in minority, you do stratified sampling so as to ensure that your train and test splits represent the true nature of your data. For more details take a look here.