Gin provides a lightweight configuration framework for Python built on dependency injection. Developers decorate functions or classes with @gin.configurable to supply default parameter values from config files or command-line arguments. It specifically targets machine learning experiments, which tend to have many nested parameters, and ships with dedicated modules for TensorFlow and PyTorch integration.