cuML is a RAPIDS library that implements machine learning algorithms and mathematical primitives on GPUs, enabling data scientists to run traditional tabular ML tasks without CUDA programming expertise. It provides Python APIs compatible with scikit-learn and supports multi-GPU operations via Dask. GPU implementations offer 10-50x speedups over CPU equivalents for algorithms like DBSCAN and NearestNeighbors.