The hyperparameter tuning process allows machine learning models to find the optimal settings for the best results from our model. Hyperparameter tuning can be time-consuming and tedious, but automated methods like grid search, random search, and Bayesian optimization can help us find optimal hyperparameters more quickly. Ray Tune is a distributed hyperparameter tuning library that can accelerate the process by trying different combinations of hyperparameters in parallel on multiple computers. We can use Ray to distribute our hyperparameter tuning across a cluster, which can significantly reduce the time needed to tune our model. By using Ray and Ray Tune, we can rapidly find optimal hyperparameters for our machine learning models, such as the digit identification model, without requiring significant code refactoring. The process allows us to efficiently improve our model's performance by distributing the work across multiple nodes in the cluster.