What is the ROC Curve and why is it important in machine learning?

I'm learning machine learning model evaluation and often come across the ROC Curve (Receiver Operating Characteristic Curve). I understand it's used to measure the performance of classification models, but I'm not sure how it works or how to interpret it.

Can someone explain what the ROC Curve represents, how it relates to the True Positive Rate (TPR) and False Positive Rate (FPR), and what the Area Under the Curve (AUC) indicates? A simple example showing how to read and compare ROC curves for different models would be very helpful.