The Partial Autocorrelation Function (PACF) measures the degree of association between a time series and its lagged values, after removing the effects of intervening lags. It is crucial for identifying the appropriate lag order in autoregressive models by revealing the direct influence of past values on future values without the interference of indirect correlations.