Apache Spark ML (Machine Learning) is a library designed to handle large-scale data processing tasks, including training machine learning models. It provides a uniform set of high-level APIs built on top of DataFrames, which are designed to make data processing fast and easy.

Here are some key features of Spark ML:

1. Algorithms: It includes common learning algorithms such as classification, regression, clustering, and collaborative filtering.
2. Feature Transformations: It offers a wide variety of transformations, including feature extraction, transformation, dimensionality reduction, and selection.
3. Pipelines: Spark ML promotes a practical workflow through its concept of pipelines. These pipelines allow you to specify a linear sequence of data transformations that will be applied to the underlying dataset.
4. Persistence: Models and entire pipelines can be saved and loaded across different languages (Scala, Java, Python).
5. Integration: It’s designed to integrate seamlessly with other popular data processing tools within the Spark ecosystem.
6. Scalability: By leveraging Spark’s core engine, it can efficiently handle large datasets that may not fit into memory. This makes it an attractive option for big data scenarios.
7. Ease of Use: Its high-level APIs make it accessible for developers with various levels of expertise.

Spark ML can be used for diverse real-world tasks, such as fraud detection, recommendation systems, predictive analytics, and more.