Is cross validation just about using different combinations of data blocks to test and train different predictive models in order to find the best model?

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Yes, it uses different blocks of the data in different combinations when sampling. The use is to assess the particular model's efficacy for inference on new unseen data.

So the end purpose is finding the best model like you said. You can compare the R^2 of the training data predictions with the cross validated predictions. If the cross validated performance is much worse, you could be confident that the model is overfitting to some level, and try reducing model complexity in order to allow the model to generalize to unseen data more accurately.

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