I understand that Stepwise regression analysis has lots of limitations, including the assumption that the predictors are not highly correlated with each other. In fact, this limitation was the most important reason that I switched to Elastic Net, as I had 75 predictors in my model, some of which are highly correlated.
Using Elastic Net, I could reduce my predictors to 21. I used these selected 21 variables in a multilinear regression model and calculated the coefficient of determination ($R^2=0.58$).
However, when I used Stepwise analysis on the same data, only 11 variables got selected, while the R-square stayed the same! Does it mean that my results from Stepwise analysis can explain a higher proportion of my outcome? If so, how can I justify the limitations of Stepwise analysis over Elastic Net when I'm getting better results?