Lasso effective in predictive models even if n > p?

My understanding is lasso regressions need not be used exclusively for variable selection when n < p.

Even if the sample size greatly exceeds the # of variables, lasso may still improve predictive accuracy by introducing bias into coefficient estimates in order to reduce variance of validation data predictions (bias/variance tradeoff).

Or am I wrong on this?

accuracy bias data datascience lasso predictions predictive predictive models reduce sample understanding validation variables variance