The No Free Lunch Theorem in optimization and machine learning states that no single algorithm is universally optimal for all possible problems; rather, an algorithm’s performance varies based on the specific dataset and context. Consequently, selecting the most suitable algorithm requires considering the particular problem domain and the associated data characteristics.