It is suggested that in the presence of few variables with medium / large sized effect, lasso regression can be used. In the presence of many variables with small/medium sized effect, ridge regression can be preferred. Conceptually, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In the presence of correlated variables, ridge regression might be the preferred choice. Additionally, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our business goal and model objective as to what is the expectation. Accordingly, decisions can be taken.