What strategy can significantly improve the accuracy of AI models?

Applying cross-validation techniques during training is a vital strategy for improving the accuracy of AI models. Cross-validation allows for the assessment of the model's performance on different subsets of the training data, helping to ensure that the model generalizes well to unseen data. By splitting the data into several training and validation sets, cross-validation reduces the risk of overfitting, where a model performs well on the training data but poorly on new data. This process provides a more reliable estimate of the model's performance, leading to improved accuracy as it learns to capture the underlying patterns in the data without becoming too tailored to a specific dataset.

In contrast, simply implementing feature selection or reducing the number of features may enhance the model's interpretability or help mitigate the curse of dimensionality, but they do not inherently guarantee an increase in accuracy. Utilizing larger datasets can be beneficial, but this should be accompanied by appropriate preprocessing and validation techniques to maximize their effectiveness, rather than merely adding data without modification. Therefore, the application of cross-validation stands out as a critical method for improving model accuracy through robust evaluation and training processes.