Detecting Multicollinearity in OLS

You are examining an OLS regression with design matrix $X$. Four methods have been proposed to detect multicollinearity among the predictor variables: 1. Inspect the eigenvalues of $X^T X$ for near-zero values. 2. Compute the condition number of $X^T X$ (ratio of largest to smallest eigenvalue) and flag large values. 3. Inspect the correlation matrix of the rows of $X$. 4. Inspect the correlation matrix of the columns of $X$. Which of these methods correctly detect multicollinearity? For each, explain why it works or why it fails.