How do I optimize Python code to experiment with ML models?

Question

I have multiple feature sets, outcomes (predictor variables) and machine learning models that would like to test. This gives me a huge number of feature-outcome-model combinations. How can I write Python code that is optimized to perform these experiments and return results without being too computationally expensive? I have been thinking of something like building pipelines on Sklearn. Please let me know if there are better ways to achieve this.