Data preprocessing involves cleaning, transforming, and organizing raw data into a structured format for analytical tasks. Essential steps include handling missing data, filtering out noise, normalizing data scales, and encoding categorical features for machine learning models. By improving data quality, it leads to more accurate and efficient algorithms. It contrasts with 'feature engineering' which focus on creating new inputs to the algorithm.