Why do I plot only one feature when visualizing predictions for a multi-feature regression model?

I am working on a regression task where my feature matrix consists of two features: a linear term and its square (quadratic feature). My model is predicting values correctly, but after numerous attempts I noticed that to visualize the predictions, only the first column of my feature matrix (i.e., the linear feature) needs to be plotted against the predictions.

Here’s the relevant part of my code

X_custom_1 = np.arange(-5, 5, 0.01).reshape(1000, 1)
X_custom_2 = X_custom_1**2
X_custom = np.append(X_custom_1, X_custom_2, axis=1)
y_pred = model.predict(X_custom)

# Debugging
print(X_custom_1.shape, X_custom_2.shape)  # Output: (1000, 1) (1000, 1)
print(X_custom.shape, y_pred.shape)        # Output: (1000, 2) (1000, 1)

# Plotting
_, ax = plt.subplots(ncols=2, figsize=(19, 7))
ax[0].scatter(X, y)  # Original data: (50, 1) (50, )
ax[0].plot(X_custom[:, 0], y_pred, color='red')  # Model predictions

In the ax[0].plot() line, I am plotting X_custom[:, 0] (the linear feature) against y_pred which can be seen below:

My question is: Why does it make sense to plot the predictions against only the first feature of the input matrix, rather than using all features? Is it because a line plot inherently works only for a single feature?

Any clarification is appreciated.