L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model’s coefficients to the loss function 1 .  It encourages sparsity in the model by shrinking some coefficients to precisely zero 2 . This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.