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Abstract Policy search methods provide a heuristic mapping between observations and decisions and have been widely used in reservoir control studies. However, recent studies have observed a tendency for policy search methods to overfit to the hydrologic data used in training, particularly the sequence of flood and drought events. This technical note develops an extension of bootstrap aggregation (bagging) and cross‐validation techniques, inspired by the machine learning literature, to improve reservoir control policy performance on out‐of‐sample hydrological sequences. We explore these methods using a case study of Folsom Reservoir, California, using control policies structured as binary trees, and streamflow resampling based on the paleo‐inflow record. Results show that calibration‐validation strategies for policy selection coupled with certain ensemble aggregation methods can improve out‐of‐sample performance in water supply and flood risk objectives over baseline performance given fixed computational costs. Our findings highlight the potential to improve policy search methodologies by leveraging these well‐established model training strategies from machine learning.