Scikit-learn model selection requires a structured approach, emphasizing best practices such as pipelines for preprocessing and model fitting. Effective strategies include k-fold cross-validation for performance estimation, hyperparameter tuning with RandomizedSearchCV, and documenting experiments for insights. Balancing model complexity with interpretability is crucial.