This is no contradiction. When using classic machine learning models you should definitely chose your features carefully, evaluate importance, check distribution / correlations as well as think about transformations like PCA or PLS as well as incorporate your domain knowledge w/ signal processing.
Still you might not have sufficient data to fit a robust model which poses the risk of overfitting. In this case regularization is a powerful tool to reduce the model complexity as @pastorsoto pointed out correctly in his very good answer. Dropout would be another potential measure to reduce overfitting, see also this thread.
Best regards
Christian