L1 regularization produces a sparse model (assuming the value of hyperparameter (C) is large enough), i.e., a model where most of its parameters equal exactly zero.
L1 implicitly performs feature selection by deciding which features are essential for prediction and which are not.
L1 is a good choice when we want to increase model explainability.
L2 is a good choice to maximize the model performance on the holdout data.
In the literature, L1 = lasso; L2 = ridge regularization.
L1-Regularization (Lasso Regression)