I read this post [Why use Lasso estimates over OLS estimates on the Lasso-identified subset of variables?](https://stats.stackexchange.com/questions/82466/why-use-lasso-estimates-over-ols-estimates-on-the-lasso-identified-subset-of-var). It says the LASSO shrinkage causes the estimates of the non-zero coefficients to be biased towards zero. So using OLS after LASSO selection is a recommended method (two-stage LASSO).

Is there any similar rule applying to elastic net? I understand that EN is the combination of ridge + LASSO. Say if I select the number of variables > the number of samples, because some of them are correlated. Then if I apply ridge regression on the selected variables, ridge will also bias the coefficients towards zero, but OLS is not applicable in this case.

So how to resolve this problem?