I am recently learning machine learning and got to know about feature selection.
I am wondering if a wrapper method like "Recursive feature elimination" provided by scikit tests cross validation on all subset of features, isn't it the best way to select features compared to other feature selection methods. Such as, Removing features with low variance.
Because, "Recursive feature elimination" checks all subset of features in the training set.
If not, please explain the reason.