Pipeline automation refers to the process of streamlining and automating the workflow of data processing and model training in machine learning. By utilizing pipelines, data scientists can bundle various steps such as data preprocessing, feature selection, and model training into a single cohesive unit. This approach not only enhances code organization but also reduces the likelihood of errors, making the modeling process more efficient. Additionally, pipeline automation facilitates easier deployment of models into production, allowing for scalable and reproducible machine learning solutions. Overall, it is a crucial practice for optimizing the machine learning lifecycle.