You are tasked with building a linear regression model for a financial services company that predicts customer loan approval rates. The dataset includes over a hundred input features, all scaled between -1 and 1, but preliminary analysis suggests many features may not contribute meaningful information to the model. The company emphasizes the importance of model interpretability and requires a solution that not only preserves the informative features but also clearly identifies and eliminates the non-informative ones without significantly increasing computational costs. Considering these constraints, which technique should be applied? Choose the best option.