diff --git a/spacy/pipeline/textcat.py b/spacy/pipeline/textcat.py
index 1a1e2cb64..858879b9e 100644
--- a/spacy/pipeline/textcat.py
+++ b/spacy/pipeline/textcat.py
@@ -72,7 +72,7 @@ subword_features = true
     "textcat",
     assigns=["doc.cats"],
     default_config={
-        "threshold": 0.0,
+        "threshold": None,
         "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
         "scorer": {"@scorers": "spacy.textcat_scorer.v1"},
     },
@@ -94,7 +94,7 @@ def make_textcat(
     nlp: Language,
     name: str,
     model: Model[List[Doc], List[Floats2d]],
-    threshold: float,
+    threshold: Optional[float],
     scorer: Optional[Callable],
 ) -> "TextCategorizer":
     """Create a TextCategorizer component. The text categorizer predicts categories
@@ -135,7 +135,7 @@ class TextCategorizer(TrainablePipe):
         model: Model,
         name: str = "textcat",
         *,
-        threshold: float,
+        threshold: Optional[float],
         scorer: Optional[Callable] = textcat_score,
     ) -> None:
         """Initialize a text categorizer for single-label classification.
@@ -144,7 +144,8 @@ class TextCategorizer(TrainablePipe):
         model (thinc.api.Model): The Thinc Model powering the pipeline component.
         name (str): The component instance name, used to add entries to the
             losses during training.
-        threshold (float): Cutoff to consider a prediction "positive".
+        threshold (Optional[float]): Unused, not needed for single-label
+            (exclusive classes) classification.
         scorer (Optional[Callable]): The scoring method. Defaults to
                 Scorer.score_cats for the attribute "cats".
 
diff --git a/website/docs/api/textcategorizer.md b/website/docs/api/textcategorizer.md
index 042b4ab76..f5f8706ec 100644
--- a/website/docs/api/textcategorizer.md
+++ b/website/docs/api/textcategorizer.md
@@ -63,7 +63,6 @@ architectures and their arguments and hyperparameters.
 > ```python
 > from spacy.pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
 > config = {
->    "threshold": 0.5,
 >    "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
 > }
 > nlp.add_pipe("textcat", config=config)
@@ -82,7 +81,7 @@ architectures and their arguments and hyperparameters.
 
 | Setting     | Description                                                                                                                                                      |
 | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~                                                                   |
+| `threshold` | Cutoff to consider a prediction "positive", relevant for `textcat_multilabel` when calculating accuracy scores. ~~float~~                                        |
 | `model`     | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
 | `scorer`    | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~                                 |
 
@@ -123,7 +122,7 @@ shortcut for this and instantiate the component using its string name and
 | `model`        | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~       |
 | `name`         | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                              |
 | _keyword-only_ |                                                                                                                                  |
-| `threshold`    | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~                                   |
+| `threshold`    | Cutoff to consider a prediction "positive", relevant for `textcat_multilabel` when calculating accuracy scores. ~~float~~        |
 | `scorer`       | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~ |
 
 ## TextCategorizer.\_\_call\_\_ {#call tag="method"}