The correct answer is False.
In k-fold cross-validation, the data is divided into k equal-sized subsets. One subset is used as the validation data, and the remaining k-1 subsets are used as the training data. The model is trained on the training data and then evaluated on the validation data. This process is repeated k times, with each subset used once as the validation data. The final model is then evaluated on the entire dataset.
The variance of the model’s performance is a measure of how much the model’s performance changes when the training data is changed. A smaller k value implies that each subset is smaller, and therefore the training data is more likely to change from one fold to the next. This means that the variance of the model’s performance is likely to be higher for a smaller k value.
In conclusion, the correct answer is False. A smaller k value does not imply less variance.