OLS is a special case of ElasticNet, Lasso, and ridge regression with the regularization parameters set to zero. (The latter two are also special cases of ElasticNet with one of the two regularization parameters set to zero.) In the presence of many predictors or multicollinearity among the predictors, OLS tends to overfit the data and regularized models usually provide better predictions, although OLS still has its place in exploratory data analysis.