There are several posts about the comparison of lasso vs. ridge. However I didn't find an explanation to my question. My question is why ridge is generating lower prediction errors in cases where the true model is non sparse. I know that lasso is designed to handle sparse models. But Lasso doesn't have to shrink coefficents exactly to zero.
Can someone can give me some intuition why ridge is performing better than lasso in situations, where the coefficients have similar size?