Variable selection is a critical step in building predictive models, as it involves identifying the most relevant predictors that contribute to the model's performance while minimizing overfitting. Effective variable selection can enhance model interpretability, reduce computational cost, and improve prediction accuracy by excluding irrelevant or redundant features.