Which of the following best describes the purpose of cross$-$validation in supervised learning?

Which of the following best describes the purpose of cross$-$validation in supervised learning?

To split the data into training and holdout sets

To reduce the dimensionality of the feature space

To randomly shuffle the data before training the model

To reduce the likelihood of making conclusions that are idiosyncratic to how the individual data points were assigned to the different sets in the partition