Distributed Machine Learning is a type of Machine Learning that uses multiple computing nodes to store, process and analyze data, allowing for faster and more efficient training of complex models. This distributed approach allows for larger datasets to be processed in a shorter amount of time, and can also help reduce the risk of overfitting. Additionally, distributed Machine Learning can be used to increase the accuracy of predictions.