When selecting $K$ for $K$-Fold Cross-Validation, we must consider the bias-variance tradeoff. $K$-Fold Cross-Validation provides $K$ estimates of the model error. The mean of these errors indicates the model's bias, where a lower mean value implies higher accuracy. The model variance is determined by the standard deviation of these errors, where a lower variance indicates less fluctuation in model performance. Usually, it is difficult to reduce both model bias and variance simultaneously.