The Receiver Operating Characteristic (ROC) Curve that you are showing helps in evaluating and comparing the performance at binary classification of machine learning models (see article). For different thresholds (not shown) of the model's output probability of the positive class, the ROC curve shows the Sensitivity (True Positive Rate) vs. 1-Specificity (False Positive Rate) for the binary classification task. Thus, you can see how changing the threshold changes the Sensitivity and Specificity. Generally, setting the threshold low results in high Sensitivity and low Specificity. As the threshold is increased, the Sensitivity decreases and Specificity increases.
The area under the ROC curve (AUC) is a measure of how well the model performs. If the model were presented with all possible pairs of positive and negative examples from your dataset, the AUC is the proportion of pairs that the model would correctly identify which is which. The maximum value that AUC can have is 1, and this is the AUC a "perfect" classifier would have. The diagonal line indicates the performance of a naïve model (a dummy classifier) that predicts randomly, and as such, the AUC for the diagonal is 0.5 (i.e., coin flip). Therefore, in your example, the Logistic Regression model has the best performance and the Random Forrest model has the worst performance.