In general, one might expect the Lasso to perform better in a setting where a relatively small number of predictors have substantial coefficients, and the remaining predictors have coefficients that are very small or that equal zero. Ridge regression will perform better when the response is a function of many predictors, all with coefficients of roughly equal size.

However, the number of predictors that is related to the response if never known a priori for real data sets. A technique such as cross-validation can be used to determine which approach is better on a particular data set.