Custom transformers in machine learning workflows enhance data preprocessing by tailoring transformations to specific dataset characteristics. Key methods include fit, which learns from data, and transform, which applies learned changes. Implementations like StandardScaler and OneHotEncoder demonstrate effective feature standardization and categorical encoding, respectively. Robust transformers handle various data types and missing values, integrating seamlessly into scikit-learn pipelines for improved model performance and code reuse.