Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.