Gini Impurity is a metric used in decision tree algorithms to measure the impurity or disorder of a dataset, with a lower value indicating a more homogeneous node. It is calculated as the probability of a randomly chosen element being incorrectly classified if it was randomly labeled according to the distribution of labels in the subset.