Why a Single Train/Test Split Can Mislead Your ML Model's Accuracy

A single 80/20 data split can produce misleading accuracy scores because the result depends heavily on which data points land in the test set. Cross-validation addresses this by dividing data into k equal folds, training the model k times, and validating on a different fold each round. This ensures every data point is used for both training and validation, producing a more reliable mean accuracy with a standard deviation. Best practices include fitting preprocessors inside each fold to prevent data leakage, stratifying splits for imbalanced datasets, and preserving a separate final test set untouched during tuning. The trade-off is k times the computational cost, but the payoff is a statistically honest performance estimate.