Checking Model Generalization: Cross-validation gives the idea about how the model will generalize to an unknown dataset
Checking Model Performance: Cross-validation helps to determine a more accurate estimate of model prediction performance
Higher Training Time: with cross-validation, we need to train the model on multiple training sets.
Expensive Computation: Cross-validation is computationally very expensive as we need to train on multiple training sets.
For more details please watch this video Cross-Validation Advantages and Disadvantages in Machine Learning
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