Distributed Machine Learning (DML) involves partitioning large-scale machine learning tasks across multiple machines or processors to enhance computational efficiency and manage extensive datasets. By distributing the workload, DML accelerates training processes and allows for the handling of models that are computationally intensive. This approach is particularly beneficial when dealing with massive datasets that are challenging to process on a single machine.