Dataset engineering involves the essential processes of collecting, cleaning, and organizing data to ensure it's high-quality and suitable for use in learning models. This includes operations like handling missing values, data augmentation, and scaling. Effective dataset engineering is crucial because the quality of data directly impacts the performance of models. It's related to data preprocessing and is foundational for developing robust, accurate learning systems.