What is the typical outcome of using K-fold cross-validation?

K-fold cross-validation is a technique used to evaluate the performance of a machine learning model by splitting the dataset into K subsets, known as folds. During the process, the model is trained K times, each time using K-1 folds as the training set and the remaining fold as the validation set. This method allows for each data point to be used for both training and validation, providing a more reliable estimate of the model’s performance.

The outcome of this process is a set of performance metrics, such as accuracy, precision, recall, or F1 score, calculated over each of the K iterations. By averaging these metrics, you obtain a comprehensive view of how well the model is likely to perform on unseen data, which reflects its generalizability. This is particularly important in avoiding overfitting, as it tests the model's robustness across different subsets of the data.

Utilizing K-fold cross-validation ensures that the performance measure is not reliant on a single split of the data, which can sometimes give misleading results, but rather provides insights based on multiple training and validation iterations. This enhances the reliability of your model evaluation, making option C the most appropriate choice.