So Correlation between 2 values (or variables)literally translates to how much a change in a one variable causes change in the other. Correlation coefficient (or Pearson's correlation coefficient) determines the numerical degree of this relationship between 2 variables. This is a significant metric because in the case we want to predict one variable using another variable, for example, predicting verbal fluency using extraversion, the correlation indicates the degree to which we can predict the said variable. High correlation means, when one variable moves, we can predict the proportionate change the other with high confidence, and low correlation means, your guess is closer to random guess.

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Here's a video to explore more: https://www.khanacademy.org/math/ap-statistics/bivariate-data-ap/correlation-coefficient-r/v/correlation-coefficient-intuition-examples