Scikit-learn is a Python library for machine learning that can be optimized by changing the solver, which allows better use of hardware with more efficient algorithms. To further speed up model building, hyperparameter tuning techniques such as grid search, random search, and Bayesian optimization can be used, with libraries like Tune-sklearn providing cutting-edge techniques and consistency with the scikit-learn API. Parallelizing or distributing training using joblib and Ray can also significantly increase model building speed, especially for models with high parallelism like random forests. By utilizing these approaches, developers can create the best scikit-learn model in the least amount of time.