A data scientist is building a binary classification model to predict equipment failure using a labeled dataset of only 1,200 records. When they use a single 80/20 train-test split, the validation accuracy fluctuates significantly depending on which records land in the test set, making it hard to judge whether one algorithm truly outperforms another. Which evaluation approach will produce the most reliable estimate of model performance given the limited data?