The article discusses a method to make machine learning more efficient by finding the best settings for its internal parameters. By using Bayesian optimization, the researchers were able to quickly identify the best configurations for a specific machine learning method called kernel ridge regression. They found that this method is faster and just as accurate as the traditional exhaustive search method. This approach can help improve the performance of machine learning models without needing expert knowledge or long computational times.