Joblib is a set of tools to provide lightweight pipelining in Python, with a focus on transparent disk caching. The 'joblib' file format is primarily used for serializing (saving) large Python objects, especially those used in scientific computing and machine learning, such as NumPy arrays, scikit-learn models, and complex data structures. It is an optimized alternative to Python's standard 'pickle' module, often providing better performance for large arrays by leveraging efficient memory mapping (via NumPy). When a Python object is 'dumped' using joblib, it is saved to a file, typically with the '.joblib' extension, allowing the object to be loaded back into memory later without needing to recompute it. This serialization is crucial for workflows where training a model or processing large datasets is time-consuming, enabling developers to save intermediate results and resume work quickly. While the underlying mechanism is similar to pickle, joblib is specifically engineered to handle large data efficiently, making it a staple in the data science ecosystem.