Yeah, the title is a bit hyperbolic. I have not used selection methods that much, but not too surprising they would have similar results to LASSO as selection or predictive method for people who think of it in terms of "feature development".
The distaste for step-wise selection comes from its typical use. If one reads Harrell's complaints quoted in the blog post carefully, quite many of them are less about the selection method but what analyst does with it, namely, interpretation of inferential statistics. When you see step-wise in the wild, practitioner often has used step-wise or other selection method and then reports the usual test-statistics and p-values for the final fitted model ... that are derived with assumptions that don't usually take into account the selection steps. It is quite unfortunate in fields where people put lot of faith in coefficient estimates, p-values and Wald confidence intervals when writing conclusions of their paper.
With LASSO and its cousins, the standard packages and literature strongly encourage the user to focus on predictions and run cross-validation right from the beginning.
The distaste for step-wise selection comes from its typical use. If one reads Harrell's complaints quoted in the blog post carefully, quite many of them are less about the selection method but what analyst does with it, namely, interpretation of inferential statistics. When you see step-wise in the wild, practitioner often has used step-wise or other selection method and then reports the usual test-statistics and p-values for the final fitted model ... that are derived with assumptions that don't usually take into account the selection steps. It is quite unfortunate in fields where people put lot of faith in coefficient estimates, p-values and Wald confidence intervals when writing conclusions of their paper.
With LASSO and its cousins, the standard packages and literature strongly encourage the user to focus on predictions and run cross-validation right from the beginning.