What is the main purpose of using cross-validation in model selection?

Using cross-validation in model selection serves the primary purpose of providing an unbiased estimate of the model's performance on unseen data. This statistical technique involves partitioning the dataset into subsets, training the model on some of these subsets while validating it on the remaining ones. By doing so, it allows for an evaluation of how well the model generalizes beyond the data it was trained on.

The significance of this approach lies in its ability to mitigate issues like overfitting, where a model may show excellent performance on the training data but fails to perform similarly on new, unseen instances. Cross-validation ensures that the performance measurement reflects the model's capability to generalize, leading to better model selection.

Other options do not align with the fundamental goal of cross-validation. Increasing the complexity of a model does not inherently improve its predictive accuracy and can lead to worse performance if overfitting occurs. Similarly, fitting the training data perfectly could indicate a lack of generalization capability. Lastly, while cross-validation can assist in evaluating models with tuning parameters, it does not eliminate the need for them; rather, it helps in selecting the optimal parameters by comparing the performance across different configurations.