Pipeline automation refers to the process of streamlining and automating the steps involved in data preprocessing, model training, and deployment in machine learning workflows. By utilizing pipelines, data scientists can bundle various tasks into a single, cohesive unit, which enhances code organization and reduces the likelihood of errors. This approach not only simplifies the management of data at each stage but also facilitates easier transitions from prototype models to production-ready applications. Ultimately, pipeline automation leads to cleaner code, fewer bugs, and improved efficiency in model validation and deployment processes.