You are developing a regression model to predict housing prices using a dataset with many features such as location, square footage, number of bedrooms, and amenities. Some features exhibit very high variance, causing overfitting. To address this, you consider applying regularization. Which regularization method should you choose to reduce overfitting while also simplifying the model by eliminating less important features, and why?