Many times identifying and addressing outliers in model selection requires a one-by-one approach. While dropping outliers is a commonly used strategy, one must make sure they fit the model first. This means you must avoid dropping data points purely based on standard deviations. Sometimes multiple outliers are needed to properly fit data, so you should always fit the model first then make necessary drops.