Yeah! And they use "feature engineering" where "feature selection" would suffice. The hard part of feature engineering is usually not selection, but creatively

Yeah! And they use "feature engineering" where "feature selection" would suffice. The hard part of feature engineering is usually not selection, but creatively generating features based on expert knowledge, etc. Also, something like a wrapper would be an overkill if you had roughly, #data/class > #features and used a relatively overfitting-resistant classifier like SVM, regularized regression, etc. (more rigorous statement of this would involve VC-dimension).