MCMLv1, or Multi-Class Multi-Label Version 1, is a framework designed for evaluating machine learning models, particularly in the context of multi-class and multi-label classification tasks. It provides a standardized approach to measure performance metrics, enabling researchers and practitioners to compare results across different models and datasets. The framework emphasizes the importance of handling multiple labels per instance, which is common in various applications such as text categorization and image tagging.