When preprocessing a dataset for machine learning, features that serve solely as unique identifiers (such as ID numbers) must be removed before feeding the data into the model. While these identifiers are convenient for distinguishing specific training examples, they carry no relevant information for prediction purposes and can introduce noise or spurious correlations into the learning process.