PyTorch Lightning is a popular framework in machine learning that offers several benefits, including reduced boilerplate code and improved scalability. To perform hyperparameter tuning, which is crucial for model selection, researchers can use Ray Tune, an industry-standard tool that provides various features such as the ability to run multiple training runs at scale and analyze results with hyperparameter analysis tools. By adding a callback to communicate with Ray Tune, users can make their PyTorch Lightning models configurable and find the best combination of hyperparameters for their model by running Ray Tune's search algorithm on a defined search space. The process is straightforward, requiring only a few lines of code, and does not require changing anything in the LightningModule. Ray Tune supports various methods for defining the search space, and users can visualize the training progress using TensorBoard. By leveraging Ray Tune with PyTorch Lightning, researchers can easily perform hyperparameter tuning and improve their model's accuracy.